| المؤلفون: |
Zhong YL; School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China.; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China., Ran JJ; School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China.; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China., Wen W; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China.; College of Resources and Environment, Yangtze University, Wuhan 430000, China., Zhang MY; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China.; College of Resources and Environment, Yangtze University, Wuhan 430000, China., Lü MQ; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China., Wu SJ; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China. |
| مستخلص: |
Small reservoirs in the Yangtze River are large in quantity and widely distributed, which have important ecological and economic benefits. It is of great significance to explore the response law of small reservoir water quality to environmental variables for improving reservoir water quality. Based on the measured water quality data of 36 small reservoirs in the upper reaches of the Yangtze River, combined with the measured water quality data, using correlation analysis, redundancy analysis, and other research methods, we divided the environmental variables into three categories:landscape configuration index, landscape composition index, and reservoir characteristic index, and explored their impact on the change in water quality index. The research results showed that:① farms were the main source of NO 3 - -N, TN, and TP, and residential land was the main source of TP. Paddy, forest land, and wasteland had positive effects on the retention and purification of N and P in reservoirs. ② The LPI of farms was significantly positively correlated with the concentrations of TN and NO 3 - -N in the reservoir, and the paddy and forest were significantly negatively correlated with the concentrations of NO 3 - -N and TN in the reservoir. The PD of farms was positively correlated with TP. The LPI was negatively correlated with TP content. ③ The correlation between average reservoir depth and reservoir water quality was the strongest. Reservoir capacity, reservoir average depth, and reservoir LSI had significant positive effects on water quality improvement. ④ Among the environmental variables of the reservoir, landscape configuration index had the highest explanatory rate (24%), followed by that of the reservoir characteristics index (11%) and landscape composition index (9%). Watershed factors were the key factors (55%), and internal factors (19%) also had a significant influence on reservoir water quality. The research results can provide a theoretical basis for controlling water quality degradation of small reservoirs by managing and optimizing the landscape characteristics of reservoirs. |
